tff.learning.models.save
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Serializes model
as a TensorFlow SavedModel to path
.
tff.learning.models.save(
model: tff.learning.models.VariableModel
,
path: str,
input_type=None
) -> None
The resulting SavedModel will contain the default serving signature, which
can be used with the TFLite converter to create a TFLite flatbuffer for
inference.
Note: The model returned by tff.learning.models.load
will not be the same
Python type as the saved model. If the model serialized using this method is
a subclass of tff.learning.models.VariableModel
, that subclass is not
returned. All
method behavior is retained, but the Python type does not cross serialization
boundaries. The return type of metric_finalizers
will be an OrderedDict of
str to tff.tensorflow.computation
(annotated TFF computations) which could
be different from that of the model before serialization.
Args
model
The tff.learning.models.VariableModel
to save.
path
The str
directory path to serialize the model to.
input_type
An optional structure of tf.TensorSpec
s representing the
expected input of model.predict_on_batch
, to override reading from
model.input_spec
. Typically this will be similar to model.input_spec
,
with any example labels removed. If None, default to
model.input_spec['x']
if the input_spec is a mapping, otherwise default
to model.input_spec[0]
.
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License , and code samples are licensed under the Apache 2.0 License . For details, see the Google Developers Site Policies . Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2024-09-20 UTC.
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{"lastModified": "Last updated 2024-09-20 UTC."}
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